Images and movies are becoming increasingly prevalent on the Internet with the adoption of inexpensive memory stores and broadband connectivity. While various forms of traditional text-based content have been indexed by and searchable with sophisticated Internet search engines for many years, the identification of relevant image and movie data has presented a greater challenge. The primary way to search the Internet for an image or a movie is to query an Internet search engine with a set of text keywords. The search engine then returns a list of images whose filename or caption contain the keywords, or images that are located in the vicinity of the keywords in a web page. Unfortunately, this technique returns a number of false positives, i.e., images that have little or nothing to do with the desired search. Moreover, the search results are typically presented in the form of a linear list whose ordering is unrelated to the images' content, which forces users to scroll through hundreds of images until they find the image of interest assuming that such image is present. This can be frustrating and very time consuming.
There is therefore a need for a system that allows images to be graphically organized based on their content. This organization may then be used in conjunction with a search engine, for example, to enable a user to determine the subjective relevancy of the images based on their content instead of the images' filename or caption text alone.